scholarly journals High-dimensional Gaussian model selection on a Gaussian design

2010 ◽  
Vol 46 (2) ◽  
pp. 480-524 ◽  
Author(s):  
Nicolas Verzelen
Keyword(s):  
Model Selection ◽  
Gaussian Model ◽  
High Dimensional ◽  
Gaussian Design

scholarly journals Graph Laplacian for image anomaly detection

2020 ◽  
Vol 31 (1-2) ◽  
Author(s):  
Francesco Verdoja ◽  
Marco Grangetto
Keyword(s):  
Anomaly Detection ◽  
State Of The Art ◽  
Computational Cost ◽  
Gaussian Model ◽  
Graph Laplacian ◽  
High Dimensional ◽  
Spatial Awareness ◽  
Detection Problem ◽  
Multivariate Gaussian Model ◽  
And Inversion

Abstract Reed–Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation. In this work, a novel graph-based solution to the image anomaly detection problem is proposed; leveraging the graph Fourier transform, we are able to overcome some of RXD’s limitations while reducing computational cost at the same time. Tests over both hyperspectral and medical images, using both synthetic and real anomalies, prove the proposed technique is able to obtain significant gains over performance by other algorithms in the state of the art.

scholarly journals Least squares after model selection in high-dimensional sparse models

Bernoulli ◽  
2013 ◽  
Vol 19 (2) ◽  
pp. 521-547 ◽  
Author(s):  
Alexandre Belloni ◽  
Victor Chernozhukov
Keyword(s):  
Model Selection ◽  
Least Squares ◽  
High Dimensional ◽  
Sparse Models

High-dimensional linear model selection motivated by multiple testing

Statistics ◽  
2019 ◽  
Vol 54 (1) ◽  
pp. 152-166
Author(s):  
Konrad Furmańczyk ◽  
Wojciech Rejchel
Keyword(s):  
Model Selection ◽  
Linear Model ◽  
Multiple Testing ◽  
High Dimensional

scholarly journals High-dimensional Ising model selection using ℓ1-regularized logistic regression

2010 ◽  
Vol 38 (3) ◽  
pp. 1287-1319 ◽  
Author(s):  
Pradeep Ravikumar ◽  
Martin J. Wainwright ◽  
John D. Lafferty
Keyword(s):  
Logistic Regression ◽  
Ising Model ◽  
Model Selection ◽  
High Dimensional
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